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Territorial vulnerability to natural hazards in Europe: a composite indicator analysis and relation to economic impacts
No full textPublisher Copyright: © 2023, The Author(s).This article presents an assessment of territorial vulnerability to natural hazards in Europe at the regional level (NUTS3). The novelty of the study lies in assessing vulnerability to natural hazards through a composite indicator analysis over a large extension (1395 territories in 32 different countries), and in analysing the relation between vulnerability and economic impacts of past disasters. For responding to the first goal, a principal component analysis (PCA) was performed over 25 indicators, previously grouped into susceptibility and coping capacity, and subsequently combined to obtain the final vulnerability. The main result is the spatial distribution of vulnerability to natural hazards across Europe through a normalised and comparative approach, which indicates that 288 out of 1395 regions presented a high or a very high level of vulnerability. They are concentrated in Eastern and Southern Europe, and in the Baltic Region, and the sum of their population lives in territories with high or very high vulnerability level, representing 20% of the total sample, i.e. 116 out of 528 million inhabitants. Regarding the methodology for analysing the relation between vulnerability and economic impacts, a spatial regression model has been used to combine hazard, exposure and vulnerability. The outcomes indicate a high level of agreement between vulnerability and the distribution of past economic impacts, which confirm that the indicator-based approach is a good proxy for assessing vulnerability to natural hazards. Knowing the distribution of vulnerability is of high relevance for targeting disaster risk management and climate change adaptation actions to the highest priority regions.This work was supported by ESPON through the ESPON-TITAN project (Territorial Impacts of Natural Disasters).Peer reviewe
Extending from Adaptation to Resilience Pathways: Perspectives from the Conceptual Framework to Key Insights
Full text linkPublisher Copyright: © The Author(s) 2025.The extent and timescale of climate change impacts remain uncertain, including global temperature increase, sea level rise, and more frequent and intense extreme events. Uncertainties are compounded by cascading effects. Nevertheless, decision-makers must take action. Adaptation pathways, an approach for developing dynamic adaptive policymaking, are widely considered suitable for planning urban or regional climate change adaptation, but often lack integration of measures for disaster risk management. This article emphasizes the need to strengthen Adaptation Pathways by bringing together explicitly slow-onset impacts and sudden climate disasters within the framework of Resilience Pathways. It explores key features of Adaptation Pathways—such as thresholds, performance assessments, and visual tools—to enhance their capacity to address extreme events and foster the integration of Climate Change Adaptation and Disaster Risk Management.Peer reviewe
Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactuals
Full text linkPublisher Copyright: © 2025This paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, and smart forestry. This research posits that integrating expert knowledge with counterfactual explanations – speculative scenarios illustrating how altering input data points could lead to different outcomes – can significantly reduce the opacity of deep learning models processing point cloud data. The proposed optimization-driven framework utilizes expert-informed ad-hoc perturbation techniques to generate meaningful counterfactual scenarios when employing state-of-the-art deep learning architectures. The optimization process minimizes a multi-criteria objective comprising counterfactual metrics such as similarity, validity, and sparsity, which are specifically tailored for point cloud datasets. These metrics provide a quantitative lens for evaluating the interpretability of the counterfactuals. Furthermore, the proposed framework allows for the definition of explicit interpretable counterfactual perturbations at its core, thereby involving the audience of the model in the counterfactual generation pipeline and ultimately, improving their overall trust in the process. Results demonstrate a notable improvement in both the interpretability of the model's decisions and the actionable insights delivered to end-users. Additionally, the study explores the role of counterfactual reasoning, coupled with expert input, in enhancing trustworthiness and enabling human-in-the-loop decision-making processes. By bridging the gap between complex data interpretations and user comprehension, this research advances the field of explainable AI, contributing to the development of transparent, accountable, and human-centered artificial intelligence systems.Peer reviewe
Feasibility Assessment of BIO-PUR Composites for Offshore Applications
Full text linkPublisher Copyright: © The Author(s) 2025.The quest for sustainable materials in offshore renewable energy is critical for mitigating the environmental concerns associated with the use of conventional composites. This study explores the potential of vegetable oil-based polyurethanes (BIO-PUR) as a sustainable alternative to petrochemical-based resins in offshore structural applications. BIO-PUR composites were fabricated, mechanically characterized, and subjected to real-world marine environments in the HarshLab floating laboratory, with exposure durations of 3 and 5 months in both atmospheric and immersion zones. Comprehensive testing, including dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and interlaminar shear strength (ILSS) assessments, showed no significant degradation in the mechanical, thermal, or chemical properties of the composites. Notably, water absorption remained minimal, and the glass transition temperature of the material (Tg) and interlaminar strength remained unchanged after exposure, highlighting the exceptional durability of BIO-PUR in harsh marine environments. These results suggest that BIO-PUR composites could not only meet but potentially surpass the performance requirements for long-term offshore applications, offering a highly promising eco-friendly alternative to traditional composites. This study provides a foundation for future research into the long-term viability of biobased materials in offshore energy systems, paving the way for more sustainable solutions in renewable energy infrastructures.Peer reviewe
Development of a new ductile heat-treated multi-component aluminium by HPDC with high-performance properties for temperature applications
Full text linkPublisher Copyright: © 2025 The AuthorsThis research aims to develop heat treatments to improve mechanical and thermal properties for a novel, heat-treatable patented multi-component AlMgSiCu aluminium produced using the High-Pressure Die Casting (HPDC) process. For this purpose, experiments were designed to investigate various parameters related to the thermal treatments, implemented in three stages. The alloy displayed excellent mechanical properties at room temperature (RT) and 200 ºC with an adjusted and optimized heat treatment: 440 °C solution treatment for 72 hours, hot-water quenching, and natural ageing. The alloy's microstructure consisted of an aluminium matrix with primary and eutectic Mg2Si and globular Al2CuMg phases, where all Al₂Cu phases were transformed into Al₂CuMg. The thermal transformation of Al2Cu into the most stable Al₂CuMg phase, significantly enhanced the alloy´s overall properties, resulting in a 40 % increase in elongation (E) under tension with a yield strength (YS) of 221 MPa, an ultimate tensile strength (UTS) of 244 MPa, and an E of 1.1 %. Additionally, it led to a 20 % improvement in ultimate compressive strength (UCS), with a 60 % increase in compressive deformation (D) at RT compared with as-cast samples. At 200ºC the tensile properties remained stable, with a YS of 226 MPa, UTS of 254 MPa, and E of 1 %, while the UCS decreased by 30 % and both YS and D remained constant with a YS of 211 MPa, UCS of 468 MPa and a D of 25.1 %. Overall, the alloy demonstrated excellent performance, achieving some of the most favourable strength-to-density ratios at 200 ºC.Peer reviewe
Using Eye-Tracking Data to Examine Response Processes in Digital Competence Assessment for Validation Purposes
Full text linkPublisher Copyright: © 2025 by the authors.Background: When measuring complex cognitive constructs, it is crucial to correctly design the evaluation items in order to trigger the intended knowledge and skills. Furthermore, assessing the validity of an assessment requires considering not only the content of the evaluation tasks, but also how examinees perform by engaging construct-relevant response processes. Objectives: We used eye-tracking techniques to examine item response processes in the assessment of digital competence. The eye-tracking observations helped to fill an ‘explanatory gap’ by providing data on the variation in response processes that cannot be captured by other common sources. Method: Specifically, we used eye movement data to validate the inferences made between claimed and observed behavior. This allowed us to interpret how participants processed the information in the items in terms of Area Of Interest (their size, placement, and order). Results and Conclusions: The gaze data provide detailed information about response strategies at the item level, profiling the examinees according to their engagement, response processes and performance/success rate. The presented evidence confirms that the response patterns of the participants who responded well do not represent an alternative to the interpretation of the results that would undermine the assessment criteria. Takeaways: Gaze-based evidence has great potential to provide complementary data about the response processes performed by examinees, thereby contributing to the validity argument.Peer reviewe
Hybrid Quantum Solvers in Production: How to Succeed in the NISQ Era?
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Hybrid quantum computing is considered the present and the future within the field of quantum computing. Far from being a passing fad, this trend cannot be considered just a stopgap to address the limitations of NISQ-era devices. The foundations linking both computing paradigms will remain robust over time. The contribution of this work is twofold: first, we describe and categorize some of the most frequently used hybrid solvers, resorting to two different taxonomies recently published in the literature. Secondly, we put a special focus on two solvers that are currently deployed in real production and that have demonstrated to be near the real industry. These solvers are the LeapHybridBQMSampler contained in D-Wave’s Hybrid Solver Service and Quantagonia’s Hybrid Solver. We analyze the performance of both methods using as benchmarks four combinatorial optimization problems.Peer reviewe
Leveraging Blockchain Technology for Secure 5G Offloading Processes †
Full text linkPublisher Copyright: © 2025 by the authors.This paper presents a secure 5G offloading mechanism leveraging Blockchain technology and Self-Sovereign Identity (SSI). The advent of 5G has significantly enhanced the capabilities of all sectors, enabling innovative applications and improving security and efficiency. However, challenges such as limited infrastructure, signal interference, and high upgrade costs persist. Offloading processes already address these issues but they require more transparency and security. This paper proposes a Blockchain-based marketplace using Hyperledger Fabric to optimize resource allocation and enhance security. This marketplace facilitates the exchange of services and resources among operators, promoting competition and flexibility. Additionally, the paper introduces an SSI-based authentication system to ensure privacy and security during the offloading process. The architecture and components of the marketplace and authentication system are detailed, along with their data models and operations. Performance evaluations indicate that the proposed solutions do not significantly degrade offloading times, making them suitable for everyday applications. As a result, the integration of Blockchain and SSI technologies enhances the security and efficiency of 5G offloading.Peer reviewe
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
No full textPublisher Copyright: © 2025 Elsevier LtdDeep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models.Peer reviewe
